devxpy / glid-3-xl-stable

Stable diffusion, but with more powerful in-painting & out-painting capabilities

Cold

Public
13.4K runs
A100 (80GB)
GitHub
License

Run with an API

Playground API Examples README Versions

Prediction

devxpy/glid-3-xl-stable:7d6a340e

Model

devxpy/glid-3-xl-stable:760f4043

thh6qyqtmjbd5adxy7w5s3pfhy

Status

Succeeded

Source

Web

Hardware

–

Total duration

87.5s

Created

over 2 years ago

Input

mask
prompt: Jon snow from the game of thrones
edit_image
num_outputs: 1
num_inference_steps: "500"

{
  "mask": "https://replicate.delivery/mgxm/e2dfe2c5-7578-421e-ab55-ae9b553243ec/face_mask.png",
  "prompt": "Jon snow from the game of thrones",
  "edit_image": "https://replicate.delivery/mgxm/8a83b0ca-2ea7-4095-8057-499f35a085d8/205032562_345087143808969_4674423480430035886_n.png",
  "num_outputs": 1,
  "num_inference_steps": "500"
}

Install Replicate’s Node.js client library:

npm install replicate

Set the REPLICATE_API_TOKEN environment variable:

export REPLICATE_API_TOKEN=<paste-your-token-here>

Find your API token in your account settings.

Import and set up the client:

import Replicate from "replicate";

const replicate = new Replicate({
  auth: process.env.REPLICATE_API_TOKEN,
});

Run devxpy/glid-3-xl-stable using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "devxpy/glid-3-xl-stable:7d6a340e1815acf2b3b2ee0fcaf830fbbcd8697e9712ca63d81930c60484d2d7",
  {
    input: {
      mask: "https://replicate.delivery/mgxm/e2dfe2c5-7578-421e-ab55-ae9b553243ec/face_mask.png",
      prompt: "Jon snow from the game of thrones",
      edit_image: "https://replicate.delivery/mgxm/8a83b0ca-2ea7-4095-8057-499f35a085d8/205032562_345087143808969_4674423480430035886_n.png",
      num_outputs: 1,
      num_inference_steps: "500"
    }
  }
);
console.log(output);

To learn more, take a look at the guide on getting started with Node.js.

Install Replicate’s Python client library:

pip install replicate

Set the REPLICATE_API_TOKEN environment variable:

export REPLICATE_API_TOKEN=<paste-your-token-here>

Find your API token in your account settings.

Import the client:

import replicate

Run devxpy/glid-3-xl-stable using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "devxpy/glid-3-xl-stable:7d6a340e1815acf2b3b2ee0fcaf830fbbcd8697e9712ca63d81930c60484d2d7",
    input={
        "mask": "https://replicate.delivery/mgxm/e2dfe2c5-7578-421e-ab55-ae9b553243ec/face_mask.png",
        "prompt": "Jon snow from the game of thrones",
        "edit_image": "https://replicate.delivery/mgxm/8a83b0ca-2ea7-4095-8057-499f35a085d8/205032562_345087143808969_4674423480430035886_n.png",
        "num_outputs": 1,
        "num_inference_steps": "500"
    }
)
print(output)

To learn more, take a look at the guide on getting started with Python.

Set the REPLICATE_API_TOKEN environment variable:

export REPLICATE_API_TOKEN=<paste-your-token-here>

Find your API token in your account settings.

Run devxpy/glid-3-xl-stable using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

curl -s -X POST \
  -H "Authorization: Bearer $REPLICATE_API_TOKEN" \
  -H "Content-Type: application/json" \
  -H "Prefer: wait" \
  -d $'{
    "version": "7d6a340e1815acf2b3b2ee0fcaf830fbbcd8697e9712ca63d81930c60484d2d7",
    "input": {
      "mask": "https://replicate.delivery/mgxm/e2dfe2c5-7578-421e-ab55-ae9b553243ec/face_mask.png",
      "prompt": "Jon snow from the game of thrones",
      "edit_image": "https://replicate.delivery/mgxm/8a83b0ca-2ea7-4095-8057-499f35a085d8/205032562_345087143808969_4674423480430035886_n.png",
      "num_outputs": 1,
      "num_inference_steps": "500"
    }
  }' \
  https://api.replicate.com/v1/predictions

To learn more, take a look at Replicate’s HTTP API reference docs.

You can run this model locally using Cog. First, install Cog:

brew install cog

If you don’t have Homebrew, there are other installation options available.

Run this to download the model and run it in your local environment:

cog predict r8.im/devxpy/glid-3-xl-stable@sha256:7d6a340e1815acf2b3b2ee0fcaf830fbbcd8697e9712ca63d81930c60484d2d7 \
  -i 'mask="https://replicate.delivery/mgxm/e2dfe2c5-7578-421e-ab55-ae9b553243ec/face_mask.png"' \
  -i 'prompt="Jon snow from the game of thrones"' \
  -i 'edit_image="https://replicate.delivery/mgxm/8a83b0ca-2ea7-4095-8057-499f35a085d8/205032562_345087143808969_4674423480430035886_n.png"' \
  -i 'num_outputs=1' \
  -i 'num_inference_steps="500"'

To learn more, take a look at the Cog documentation.

Run this to download the model and run it in your local environment:

docker run -d -p 5000:5000 --gpus=all r8.im/devxpy/glid-3-xl-stable@sha256:7d6a340e1815acf2b3b2ee0fcaf830fbbcd8697e9712ca63d81930c60484d2d7
curl -s -X POST \
  -H "Content-Type: application/json" \
  -d $'{
    "input": {
      "mask": "https://replicate.delivery/mgxm/e2dfe2c5-7578-421e-ab55-ae9b553243ec/face_mask.png",
      "prompt": "Jon snow from the game of thrones",
      "edit_image": "https://replicate.delivery/mgxm/8a83b0ca-2ea7-4095-8057-499f35a085d8/205032562_345087143808969_4674423480430035886_n.png",
      "num_outputs": 1,
      "num_inference_steps": "500"
    }
  }' \
  http://localhost:5000/predictions

To learn more, take a look at the Cog documentation.

Output

{
  "completed_at": "2022-10-10T19:35:56.409213Z",
  "created_at": "2022-10-10T19:34:28.966972Z",
  "data_removed": false,
  "error": null,
  "id": "thh6qyqtmjbd5adxy7w5s3pfhy",
  "input": {
    "mask": "https://replicate.delivery/mgxm/e2dfe2c5-7578-421e-ab55-ae9b553243ec/face_mask.png",
    "prompt": "Jon snow from the game of thrones",
    "edit_image": "https://replicate.delivery/mgxm/8a83b0ca-2ea7-4095-8057-499f35a085d8/205032562_345087143808969_4674423480430035886_n.png",
    "num_outputs": 1,
    "num_inference_steps": "500"
  },
  "logs": "$ /root/.pyenv/versions/3.10.7/bin/python sample.py --model_path inpaint.pt --edit /tmp/tmpc_o91y0t205032562_345087143808969_4674423480430035886_n.png --mask /tmp/tmpvkqeu1cpface_mask.png --steps 500 --text Jon snow from the game of thrones --num_batches 1\nUsing device: cuda:0\nmaking attention of type 'vanilla' with 512 in_channels\nWorking with z of shape (1, 4, 32, 32) = 4096 dimensions.\nmaking attention of type 'vanilla' with 512 in_channels\nSome weights of the model checkpoint at openai/clip-vit-large-patch14 were not used when initializing CLIPTextModel: ['vision_model.encoder.layers.19.self_attn.k_proj.bias', 'vision_model.encoder.layers.21.layer_norm1.weight', 'vision_model.encoder.layers.0.self_attn.out_proj.bias', 'vision_model.encoder.layers.14.layer_norm1.bias', 'vision_model.encoder.layers.21.mlp.fc2.weight', 'vision_model.encoder.layers.3.mlp.fc1.bias', 'vision_model.encoder.layers.11.mlp.fc1.weight', 'vision_model.encoder.layers.3.self_attn.v_proj.bias', 'vision_model.encoder.layers.10.mlp.fc2.weight', 'vision_model.encoder.layers.8.self_attn.q_proj.bias', 'vision_model.encoder.layers.6.layer_norm1.bias', 'vision_model.encoder.layers.18.self_attn.k_proj.bias', 'vision_model.encoder.layers.8.layer_norm2.bias', 'vision_model.encoder.layers.20.mlp.fc1.bias', 'vision_model.encoder.layers.2.layer_norm1.bias', 'vision_model.encoder.layers.17.self_attn.v_proj.bias', 'vision_model.encoder.layers.1.self_attn.q_proj.bias', 'vision_model.encoder.layers.8.mlp.fc1.weight', 'vision_model.encoder.layers.13.layer_norm1.bias', 'vision_model.encoder.layers.21.self_attn.q_proj.weight', 'vision_model.encoder.layers.13.self_attn.k_proj.bias', 'vision_model.encoder.layers.10.self_attn.v_proj.bias', 'vision_model.encoder.layers.6.layer_norm2.weight', 'vision_model.encoder.layers.11.layer_norm2.weight', 'vision_model.encoder.layers.18.mlp.fc2.weight', 'vision_model.encoder.layers.8.layer_norm1.bias', 'vision_model.encoder.layers.4.mlp.fc2.weight', 'vision_model.encoder.layers.2.self_attn.out_proj.weight', 'vision_model.encoder.layers.8.mlp.fc1.bias', 'vision_model.encoder.layers.11.mlp.fc2.bias', 'vision_model.encoder.layers.17.layer_norm1.bias', 'vision_model.encoder.layers.2.layer_norm2.weight', 'vision_model.embeddings.position_ids', 'vision_model.encoder.layers.2.mlp.fc1.weight', 'vision_model.encoder.layers.20.self_attn.v_proj.bias', 'vision_model.encoder.layers.17.layer_norm2.bias', 'vision_model.encoder.layers.4.self_attn.out_proj.weight', 'vision_model.encoder.layers.4.self_attn.v_proj.weight', 'vision_model.encoder.layers.17.self_attn.q_proj.weight', 'vision_model.encoder.layers.20.self_attn.v_proj.weight', 'vision_model.encoder.layers.7.mlp.fc2.weight', 'vision_model.encoder.layers.3.layer_norm1.weight', 'vision_model.encoder.layers.22.self_attn.v_proj.weight', 'vision_model.encoder.layers.21.layer_norm1.bias', 'vision_model.encoder.layers.2.mlp.fc2.bias', 'vision_model.encoder.layers.4.self_attn.q_proj.bias', 'vision_model.encoder.layers.6.self_attn.k_proj.weight', 'vision_model.encoder.layers.6.layer_norm2.bias', 'vision_model.encoder.layers.11.mlp.fc2.weight', 'vision_model.encoder.layers.15.mlp.fc1.bias', 'vision_model.encoder.layers.15.layer_norm2.weight', 'vision_model.encoder.layers.4.self_attn.k_proj.weight', 'vision_model.encoder.layers.9.self_attn.v_proj.weight', 'vision_model.encoder.layers.16.self_attn.q_proj.weight', 'vision_model.encoder.layers.10.layer_norm1.bias', 'vision_model.encoder.layers.9.self_attn.k_proj.bias', 'vision_model.encoder.layers.7.self_attn.q_proj.weight', 'vision_model.encoder.layers.13.self_attn.q_proj.bias', 'vision_model.encoder.layers.5.layer_norm2.bias', 'vision_model.encoder.layers.15.self_attn.q_proj.weight', 'vision_model.encoder.layers.15.mlp.fc1.weight', 'vision_model.encoder.layers.21.self_attn.out_proj.weight', 'vision_model.encoder.layers.13.self_attn.q_proj.weight', 'vision_model.encoder.layers.19.self_attn.q_proj.weight', 'vision_model.encoder.layers.17.self_attn.out_proj.weight', 'vision_model.encoder.layers.10.self_attn.out_proj.bias', 'vision_model.encoder.layers.19.mlp.fc2.weight', 'vision_model.encoder.layers.13.self_attn.out_proj.bias', 'vision_model.encoder.layers.16.mlp.fc2.weight', 'vision_model.encoder.layers.3.mlp.fc1.weight', 'vision_model.encoder.layers.4.mlp.fc1.bias', 'vision_model.encoder.layers.0.mlp.fc2.bias', 'vision_model.encoder.layers.21.mlp.fc2.bias', 'vision_model.encoder.layers.18.layer_norm2.bias', 'vision_model.encoder.layers.23.mlp.fc2.bias', 'vision_model.encoder.layers.8.mlp.fc2.bias', 'vision_model.encoder.layers.21.self_attn.k_proj.weight', 'vision_model.encoder.layers.7.mlp.fc1.bias', 'vision_model.encoder.layers.19.layer_norm1.weight', 'vision_model.encoder.layers.0.layer_norm1.weight', 'vision_model.encoder.layers.4.mlp.fc1.weight', 'vision_model.encoder.layers.16.self_attn.q_proj.bias', 'vision_model.encoder.layers.1.layer_norm2.bias', 'vision_model.encoder.layers.20.layer_norm2.bias', 'vision_model.encoder.layers.12.self_attn.k_proj.bias', 'vision_model.encoder.layers.4.layer_norm1.weight', 'vision_model.encoder.layers.20.self_attn.q_proj.bias', 'vision_model.encoder.layers.6.self_attn.q_proj.bias', 'vision_model.encoder.layers.3.layer_norm2.weight', 'vision_model.encoder.layers.4.self_attn.k_proj.bias', 'vision_model.encoder.layers.5.self_attn.out_proj.bias', 'vision_model.encoder.layers.10.mlp.fc1.weight', 'vision_model.encoder.layers.17.self_attn.out_proj.bias', 'vision_model.encoder.layers.5.layer_norm1.weight', 'vision_model.encoder.layers.7.mlp.fc1.weight', 'vision_model.encoder.layers.2.self_attn.q_proj.weight', 'vision_model.encoder.layers.7.layer_norm2.bias', 'vision_model.encoder.layers.0.mlp.fc1.bias', 'vision_model.encoder.layers.8.self_attn.v_proj.weight', 'text_projection.weight', 'vision_model.encoder.layers.6.mlp.fc1.weight', 'vision_model.encoder.layers.20.layer_norm1.weight', 'vision_model.encoder.layers.21.self_attn.v_proj.weight', 'vision_model.encoder.layers.8.self_attn.k_proj.weight', 'vision_model.encoder.layers.13.self_attn.v_proj.bias', 'vision_model.encoder.layers.8.self_attn.out_proj.weight', 'vision_model.encoder.layers.13.self_attn.k_proj.weight', 'vision_model.encoder.layers.8.self_attn.out_proj.bias', 'vision_model.encoder.layers.12.layer_norm1.bias', 'vision_model.encoder.layers.7.self_attn.out_proj.bias', 'vision_model.encoder.layers.20.layer_norm1.bias', 'vision_model.encoder.layers.15.self_attn.k_proj.weight', 'vision_model.encoder.layers.22.mlp.fc1.weight', 'vision_model.encoder.layers.11.self_attn.q_proj.weight', 'vision_model.encoder.layers.1.self_attn.k_proj.bias', 'vision_model.encoder.layers.14.self_attn.k_proj.weight', 'vision_model.encoder.layers.22.self_attn.k_proj.bias', 'vision_model.encoder.layers.3.self_attn.q_proj.bias', 'vision_model.encoder.layers.12.mlp.fc1.bias', 'vision_model.encoder.layers.8.layer_norm2.weight', 'vision_model.encoder.layers.19.self_attn.k_proj.weight', 'vision_model.encoder.layers.17.layer_norm1.weight', 'vision_model.encoder.layers.17.self_attn.k_proj.weight', 'vision_model.encoder.layers.13.layer_norm1.weight', 'vision_model.encoder.layers.22.self_attn.out_proj.bias', 'vision_model.encoder.layers.8.self_attn.k_proj.bias', 'vision_model.encoder.layers.10.self_attn.k_proj.bias', 'vision_model.encoder.layers.18.self_attn.v_proj.weight', 'vision_model.encoder.layers.18.self_attn.out_proj.bias', 'vision_model.encoder.layers.15.layer_norm1.weight', 'vision_model.encoder.layers.13.mlp.fc2.bias', 'vision_model.encoder.layers.23.layer_norm1.bias', 'vision_model.encoder.layers.7.self_attn.q_proj.bias', 'vision_model.encoder.layers.18.layer_norm1.bias', 'vision_model.encoder.layers.23.mlp.fc1.weight', 'vision_model.encoder.layers.16.self_attn.v_proj.weight', 'vision_model.encoder.layers.20.self_attn.k_proj.weight', 'vision_model.encoder.layers.11.self_attn.v_proj.bias', 'vision_model.encoder.layers.19.mlp.fc2.bias', 'vision_model.encoder.layers.13.mlp.fc1.bias', 'vision_model.encoder.layers.3.self_attn.out_proj.bias', 'vision_model.encoder.layers.3.mlp.fc2.bias', 'vision_model.encoder.layers.2.self_attn.v_proj.weight', 'vision_model.encoder.layers.5.mlp.fc1.bias', 'vision_model.encoder.layers.5.mlp.fc2.bias', 'vision_model.encoder.layers.1.mlp.fc2.bias', 'vision_model.encoder.layers.19.layer_norm2.bias', 'vision_model.encoder.layers.15.layer_norm1.bias', 'vision_model.encoder.layers.16.self_attn.k_proj.bias', 'vision_model.encoder.layers.3.self_attn.k_proj.weight', 'vision_model.encoder.layers.3.layer_norm2.bias', 'vision_model.encoder.layers.18.mlp.fc1.bias', 'vision_model.encoder.layers.7.layer_norm2.weight', 'vision_model.encoder.layers.2.self_attn.v_proj.bias', 'vision_model.encoder.layers.3.self_attn.k_proj.bias', 'vision_model.encoder.layers.0.self_attn.v_proj.weight', 'vision_model.encoder.layers.0.self_attn.q_proj.bias', 'vision_model.encoder.layers.12.self_attn.v_proj.bias', 'vision_model.encoder.layers.14.layer_norm1.weight', 'vision_model.encoder.layers.4.layer_norm2.bias', 'vision_model.encoder.layers.20.self_attn.out_proj.bias', 'vision_model.encoder.layers.12.self_attn.out_proj.bias', 'vision_model.encoder.layers.6.mlp.fc2.weight', 'vision_model.encoder.layers.21.self_attn.out_proj.bias', 'vision_model.encoder.layers.22.mlp.fc2.bias', 'vision_model.encoder.layers.18.mlp.fc2.bias', 'vision_model.embeddings.patch_embedding.weight', 'vision_model.encoder.layers.9.self_attn.v_proj.bias', 'vision_model.encoder.layers.13.self_attn.v_proj.weight', 'vision_model.encoder.layers.9.layer_norm2.weight', 'vision_model.encoder.layers.10.layer_norm2.bias', 'vision_model.encoder.layers.22.layer_norm2.bias', 'vision_model.encoder.layers.22.layer_norm2.weight', 'vision_model.encoder.layers.0.self_attn.k_proj.bias', 'vision_model.encoder.layers.0.self_attn.k_proj.weight', 'vision_model.encoder.layers.7.self_attn.v_proj.weight', 'vision_model.encoder.layers.3.self_attn.q_proj.weight', 'vision_model.encoder.layers.16.self_attn.v_proj.bias', 'vision_model.encoder.layers.23.self_attn.out_proj.weight', 'vision_model.encoder.layers.10.self_attn.out_proj.weight', 'vision_model.encoder.layers.14.self_attn.v_proj.bias', 'vision_model.encoder.layers.4.mlp.fc2.bias', 'vision_model.encoder.layers.11.self_attn.v_proj.weight', 'vision_model.encoder.layers.12.mlp.fc2.weight', 'vision_model.encoder.layers.7.self_attn.v_proj.bias', 'vision_model.encoder.layers.1.self_attn.q_proj.weight', 'vision_model.encoder.layers.19.layer_norm2.weight', 'vision_model.encoder.layers.21.layer_norm2.bias', 'vision_model.encoder.layers.2.self_attn.k_proj.weight', 'vision_model.encoder.layers.18.self_attn.k_proj.weight', 'vision_model.encoder.layers.13.self_attn.out_proj.weight', 'vision_model.encoder.layers.21.self_attn.q_proj.bias', 'vision_model.encoder.layers.20.self_attn.out_proj.weight', 'vision_model.encoder.layers.16.self_attn.k_proj.weight', 'vision_model.encoder.layers.6.self_attn.v_proj.weight', 'vision_model.encoder.layers.11.self_attn.out_proj.weight', 'vision_model.encoder.layers.2.layer_norm1.weight', 'vision_model.encoder.layers.7.layer_norm1.bias', 'vision_model.encoder.layers.14.self_attn.v_proj.weight', 'vision_model.encoder.layers.16.layer_norm1.bias', 'vision_model.encoder.layers.15.layer_norm2.bias', 'vision_model.encoder.layers.16.mlp.fc1.bias', 'vision_model.encoder.layers.20.mlp.fc2.bias', 'vision_model.encoder.layers.23.self_attn.v_proj.bias', 'vision_model.encoder.layers.3.mlp.fc2.weight', 'vision_model.encoder.layers.23.layer_norm2.weight', 'vision_model.encoder.layers.20.self_attn.k_proj.bias', 'vision_model.encoder.layers.6.layer_norm1.weight', 'vision_model.encoder.layers.1.self_attn.out_proj.bias', 'vision_model.encoder.layers.6.mlp.fc2.bias', 'vision_model.encoder.layers.22.self_attn.q_proj.bias', 'vision_model.encoder.layers.9.mlp.fc2.bias', 'vision_model.encoder.layers.7.mlp.fc2.bias', 'vision_model.encoder.layers.23.layer_norm1.weight', 'vision_model.encoder.layers.6.self_attn.v_proj.bias', 'vision_model.encoder.layers.4.self_attn.out_proj.bias', 'vision_model.encoder.layers.21.layer_norm2.weight', 'vision_model.encoder.layers.2.mlp.fc2.weight', 'vision_model.encoder.layers.0.self_attn.out_proj.weight', 'vision_model.encoder.layers.6.self_attn.k_proj.bias', 'vision_model.encoder.layers.22.mlp.fc2.weight', 'vision_model.embeddings.position_embedding.weight', 'vision_model.encoder.layers.15.mlp.fc2.weight', 'vision_model.encoder.layers.3.layer_norm1.bias', 'vision_model.encoder.layers.5.self_attn.v_proj.weight', 'vision_model.encoder.layers.10.mlp.fc1.bias', 'vision_model.encoder.layers.2.layer_norm2.bias', 'vision_model.encoder.layers.16.self_attn.out_proj.bias', 'vision_model.encoder.layers.21.mlp.fc1.bias', 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'vision_model.encoder.layers.19.mlp.fc1.bias', 'vision_model.encoder.layers.22.self_attn.out_proj.weight', 'vision_model.encoder.layers.5.self_attn.k_proj.weight', 'vision_model.encoder.layers.12.self_attn.v_proj.weight', 'vision_model.encoder.layers.1.layer_norm1.weight', 'vision_model.encoder.layers.4.self_attn.q_proj.weight', 'vision_model.encoder.layers.12.self_attn.q_proj.weight', 'vision_model.encoder.layers.9.layer_norm1.weight', 'vision_model.encoder.layers.15.mlp.fc2.bias', 'vision_model.encoder.layers.16.mlp.fc2.bias', 'vision_model.encoder.layers.14.mlp.fc2.bias', 'vision_model.encoder.layers.19.self_attn.v_proj.bias', 'vision_model.encoder.layers.2.self_attn.q_proj.bias', 'vision_model.encoder.layers.5.self_attn.k_proj.bias', 'vision_model.encoder.layers.12.self_attn.k_proj.weight', 'vision_model.encoder.layers.19.self_attn.out_proj.weight', 'vision_model.encoder.layers.19.self_attn.out_proj.bias', 'vision_model.encoder.layers.4.layer_norm2.weight', 'vision_model.encoder.layers.14.mlp.fc2.weight', 'vision_model.encoder.layers.12.mlp.fc2.bias', 'vision_model.post_layernorm.weight', 'vision_model.encoder.layers.0.mlp.fc2.weight', 'vision_model.encoder.layers.14.self_attn.k_proj.bias', 'vision_model.encoder.layers.1.mlp.fc1.weight', 'vision_model.encoder.layers.22.layer_norm1.weight', 'vision_model.encoder.layers.17.mlp.fc1.weight', 'vision_model.encoder.layers.9.mlp.fc1.weight', 'logit_scale', 'vision_model.encoder.layers.12.layer_norm2.weight', 'vision_model.encoder.layers.9.self_attn.out_proj.weight', 'vision_model.encoder.layers.11.layer_norm1.bias', 'vision_model.encoder.layers.16.mlp.fc1.weight', 'vision_model.encoder.layers.18.self_attn.q_proj.bias', 'vision_model.encoder.layers.23.layer_norm2.bias', 'vision_model.encoder.layers.17.self_attn.k_proj.bias', 'vision_model.encoder.layers.18.mlp.fc1.weight']\n- This IS expected if you are initializing CLIPTextModel from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n- This IS NOT expected if you are initializing CLIPTextModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n/src/sample.py:354: DeprecationWarning: ANTIALIAS is deprecated and will be removed in Pillow 10 (2023-07-01). Use Resampling.LANCZOS instead.\n  mask_image = mask_image.resize((input_image.shape[3],input_image.shape[2]), Image.ANTIALIAS)\n\n  0%|          | 0/500 [00:00<?, ?it/s]\n  0%|          | 1/500 [00:00<05:47,  1.44it/s]\n  0%|          | 2/500 [00:01<04:35,  1.80it/s]\n  1%|          | 3/500 [00:01<04:13,  1.96it/s]\n  1%|          | 4/500 [00:01<02:55,  2.83it/s]\n  1%|          | 5/500 [00:01<02:12,  3.74it/s]\n  1%|          | 6/500 [00:01<01:46,  4.65it/s]\n  1%|▏         | 7/500 [00:02<01:29,  5.49it/s]\n  2%|▏         | 8/500 [00:02<01:19,  6.23it/s]\n  2%|▏         | 9/500 [00:02<01:11,  6.83it/s]\n  2%|▏         | 10/500 [00:02<01:06,  7.34it/s]\n  2%|▏         | 11/500 [00:02<01:03,  7.73it/s]\n  2%|▏         | 12/500 [00:02<01:00,  8.03it/s]\n  3%|▎         | 13/500 [00:02<00:59,  8.24it/s]\n  3%|▎         | 14/500 [00:02<00:57,  8.40it/s]\n  3%|▎         | 15/500 [00:02<00:56,  8.51it/s]\n  3%|▎         | 16/500 [00:03<00:56,  8.59it/s]\n  3%|▎         | 17/500 [00:03<00:55, 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  "metrics": {
    "predict_time": 87.166102,
    "total_time": 87.442241
  },
  "output": [
    "https://replicate.delivery/mgxm/9143aab6-0adf-4c5b-a58c-e38166a48c58/00000.png"
  ],
  "started_at": "2022-10-10T19:34:29.243111Z",
  "status": "succeeded",
  "urls": {
    "get": "https://api.replicate.com/v1/predictions/thh6qyqtmjbd5adxy7w5s3pfhy",
    "cancel": "https://api.replicate.com/v1/predictions/thh6qyqtmjbd5adxy7w5s3pfhy/cancel"
  },
  "version": "760f4043f4f53f4bb5e9b3d1f9268030ae514638e77732916344f1dd4b305607"
}

Generated in

87.2 seconds

Tweak it ShareReport

$ /root/.pyenv/versions/3.10.7/bin/python sample.py --model_path inpaint.pt --edit /tmp/tmpc_o91y0t205032562_345087143808969_4674423480430035886_n.png --mask /tmp/tmpvkqeu1cpface_mask.png --steps 500 --text Jon snow from the game of thrones --num_batches 1
Using device: cuda:0
making attention of type 'vanilla' with 512 in_channels
Working with z of shape (1, 4, 32, 32) = 4096 dimensions.
making attention of type 'vanilla' with 512 in_channels
Some weights of the model checkpoint at openai/clip-vit-large-patch14 were not used when initializing CLIPTextModel: ['vision_model.encoder.layers.19.self_attn.k_proj.bias', 'vision_model.encoder.layers.21.layer_norm1.weight', 'vision_model.encoder.layers.0.self_attn.out_proj.bias', 'vision_model.encoder.layers.14.layer_norm1.bias', 'vision_model.encoder.layers.21.mlp.fc2.weight', 'vision_model.encoder.layers.3.mlp.fc1.bias', 'vision_model.encoder.layers.11.mlp.fc1.weight', 'vision_model.encoder.layers.3.self_attn.v_proj.bias', 'vision_model.encoder.layers.10.mlp.fc2.weight', 'vision_model.encoder.layers.8.self_attn.q_proj.bias', 'vision_model.encoder.layers.6.layer_norm1.bias', 'vision_model.encoder.layers.18.self_attn.k_proj.bias', 'vision_model.encoder.layers.8.layer_norm2.bias', 'vision_model.encoder.layers.20.mlp.fc1.bias', 'vision_model.encoder.layers.2.layer_norm1.bias', 'vision_model.encoder.layers.17.self_attn.v_proj.bias', 'vision_model.encoder.layers.1.self_attn.q_proj.bias', 'vision_model.encoder.layers.8.mlp.fc1.weight', 'vision_model.encoder.layers.13.layer_norm1.bias', 'vision_model.encoder.layers.21.self_attn.q_proj.weight', 'vision_model.encoder.layers.13.self_attn.k_proj.bias', 'vision_model.encoder.layers.10.self_attn.v_proj.bias', 'vision_model.encoder.layers.6.layer_norm2.weight', 'vision_model.encoder.layers.11.layer_norm2.weight', 'vision_model.encoder.layers.18.mlp.fc2.weight', 'vision_model.encoder.layers.8.layer_norm1.bias', 'vision_model.encoder.layers.4.mlp.fc2.weight', 'vision_model.encoder.layers.2.self_attn.out_proj.weight', 'vision_model.encoder.layers.8.mlp.fc1.bias', 'vision_model.encoder.layers.11.mlp.fc2.bias', 'vision_model.encoder.layers.17.layer_norm1.bias', 'vision_model.encoder.layers.2.layer_norm2.weight', 'vision_model.embeddings.position_ids', 'vision_model.encoder.layers.2.mlp.fc1.weight', 'vision_model.encoder.layers.20.self_attn.v_proj.bias', 'vision_model.encoder.layers.17.layer_norm2.bias', 'vision_model.encoder.layers.4.self_attn.out_proj.weight', 'vision_model.encoder.layers.4.self_attn.v_proj.weight', 'vision_model.encoder.layers.17.self_attn.q_proj.weight', 'vision_model.encoder.layers.20.self_attn.v_proj.weight', 'vision_model.encoder.layers.7.mlp.fc2.weight', 'vision_model.encoder.layers.3.layer_norm1.weight', 'vision_model.encoder.layers.22.self_attn.v_proj.weight', 'vision_model.encoder.layers.21.layer_norm1.bias', 'vision_model.encoder.layers.2.mlp.fc2.bias', 'vision_model.encoder.layers.4.self_attn.q_proj.bias', 'vision_model.encoder.layers.6.self_attn.k_proj.weight', 'vision_model.encoder.layers.6.layer_norm2.bias', 'vision_model.encoder.layers.11.mlp.fc2.weight', 'vision_model.encoder.layers.15.mlp.fc1.bias', 'vision_model.encoder.layers.15.layer_norm2.weight', 'vision_model.encoder.layers.4.self_attn.k_proj.weight', 'vision_model.encoder.layers.9.self_attn.v_proj.weight', 'vision_model.encoder.layers.16.self_attn.q_proj.weight', 'vision_model.encoder.layers.10.layer_norm1.bias', 'vision_model.encoder.layers.9.self_attn.k_proj.bias', 'vision_model.encoder.layers.7.self_attn.q_proj.weight', 'vision_model.encoder.layers.13.self_attn.q_proj.bias', 'vision_model.encoder.layers.5.layer_norm2.bias', 'vision_model.encoder.layers.15.self_attn.q_proj.weight', 'vision_model.encoder.layers.15.mlp.fc1.weight', 'vision_model.encoder.layers.21.self_attn.out_proj.weight', 'vision_model.encoder.layers.13.self_attn.q_proj.weight', 'vision_model.encoder.layers.19.self_attn.q_proj.weight', 'vision_model.encoder.layers.17.self_attn.out_proj.weight', 'vision_model.encoder.layers.10.self_attn.out_proj.bias', 'vision_model.encoder.layers.19.mlp.fc2.weight', 'vision_model.encoder.layers.13.self_attn.out_proj.bias', 'vision_model.encoder.layers.16.mlp.fc2.weight', 'vision_model.encoder.layers.3.mlp.fc1.weight', 'vision_model.encoder.layers.4.mlp.fc1.bias', 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- This IS expected if you are initializing CLIPTextModel from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).
- This IS NOT expected if you are initializing CLIPTextModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).
/src/sample.py:354: DeprecationWarning: ANTIALIAS is deprecated and will be removed in Pillow 10 (2023-07-01). Use Resampling.LANCZOS instead.
  mask_image = mask_image.resize((input_image.shape[3],input_image.shape[2]), Image.ANTIALIAS)

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Prediction

devxpy/glid-3-xl-stable:7d6a340e

Model

devxpy/glid-3-xl-stable:773fe7b9

ymcrh3qlzrepri4btsdtlufjua

Status

Succeeded

Source

Web

Hardware

–

Total duration

8.6s

Created

over 2 years ago by @devxpy

Input

fp32: true
prompt: Jon snow from the game of thrones
init_image
num_outputs: 1
skip_timesteps: 50
num_inference_steps: "100"

{
  "fp32": true,
  "prompt": "Jon snow from the game of thrones",
  "init_image": "https://replicate.delivery/mgxm/a5f6ab5d-713a-441e-ac98-fac49c74c3b3/205032562_345087143808969_4674423480430035886_n.png",
  "num_outputs": 1,
  "skip_timesteps": 50,
  "num_inference_steps": "100"
}

Install Replicate’s Node.js client library:

npm install replicate

Set the REPLICATE_API_TOKEN environment variable:

export REPLICATE_API_TOKEN=<paste-your-token-here>

Find your API token in your account settings.

Import and set up the client:

import Replicate from "replicate";

const replicate = new Replicate({
  auth: process.env.REPLICATE_API_TOKEN,
});

Run devxpy/glid-3-xl-stable using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "devxpy/glid-3-xl-stable:7d6a340e1815acf2b3b2ee0fcaf830fbbcd8697e9712ca63d81930c60484d2d7",
  {
    input: {
      prompt: "Jon snow from the game of thrones",
      init_image: "https://replicate.delivery/mgxm/a5f6ab5d-713a-441e-ac98-fac49c74c3b3/205032562_345087143808969_4674423480430035886_n.png",
      num_outputs: 1,
      skip_timesteps: 50,
      num_inference_steps: "100"
    }
  }
);
console.log(output);

To learn more, take a look at the guide on getting started with Node.js.

Install Replicate’s Python client library:

pip install replicate

Set the REPLICATE_API_TOKEN environment variable:

export REPLICATE_API_TOKEN=<paste-your-token-here>

Find your API token in your account settings.

Import the client:

import replicate

Run devxpy/glid-3-xl-stable using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "devxpy/glid-3-xl-stable:7d6a340e1815acf2b3b2ee0fcaf830fbbcd8697e9712ca63d81930c60484d2d7",
    input={
        "prompt": "Jon snow from the game of thrones",
        "init_image": "https://replicate.delivery/mgxm/a5f6ab5d-713a-441e-ac98-fac49c74c3b3/205032562_345087143808969_4674423480430035886_n.png",
        "num_outputs": 1,
        "skip_timesteps": 50,
        "num_inference_steps": "100"
    }
)
print(output)

To learn more, take a look at the guide on getting started with Python.

Set the REPLICATE_API_TOKEN environment variable:

export REPLICATE_API_TOKEN=<paste-your-token-here>

Find your API token in your account settings.

Run devxpy/glid-3-xl-stable using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

curl -s -X POST \
  -H "Authorization: Bearer $REPLICATE_API_TOKEN" \
  -H "Content-Type: application/json" \
  -H "Prefer: wait" \
  -d $'{
    "version": "7d6a340e1815acf2b3b2ee0fcaf830fbbcd8697e9712ca63d81930c60484d2d7",
    "input": {
      "prompt": "Jon snow from the game of thrones",
      "init_image": "https://replicate.delivery/mgxm/a5f6ab5d-713a-441e-ac98-fac49c74c3b3/205032562_345087143808969_4674423480430035886_n.png",
      "num_outputs": 1,
      "skip_timesteps": 50,
      "num_inference_steps": "100"
    }
  }' \
  https://api.replicate.com/v1/predictions

To learn more, take a look at Replicate’s HTTP API reference docs.

You can run this model locally using Cog. First, install Cog:

brew install cog

If you don’t have Homebrew, there are other installation options available.

Run this to download the model and run it in your local environment:

cog predict r8.im/devxpy/glid-3-xl-stable@sha256:7d6a340e1815acf2b3b2ee0fcaf830fbbcd8697e9712ca63d81930c60484d2d7 \
  -i 'prompt="Jon snow from the game of thrones"' \
  -i 'init_image="https://replicate.delivery/mgxm/a5f6ab5d-713a-441e-ac98-fac49c74c3b3/205032562_345087143808969_4674423480430035886_n.png"' \
  -i 'num_outputs=1' \
  -i 'skip_timesteps=50' \
  -i 'num_inference_steps="100"'

To learn more, take a look at the Cog documentation.

Run this to download the model and run it in your local environment:

docker run -d -p 5000:5000 --gpus=all r8.im/devxpy/glid-3-xl-stable@sha256:7d6a340e1815acf2b3b2ee0fcaf830fbbcd8697e9712ca63d81930c60484d2d7
curl -s -X POST \
  -H "Content-Type: application/json" \
  -d $'{
    "input": {
      "prompt": "Jon snow from the game of thrones",
      "init_image": "https://replicate.delivery/mgxm/a5f6ab5d-713a-441e-ac98-fac49c74c3b3/205032562_345087143808969_4674423480430035886_n.png",
      "num_outputs": 1,
      "skip_timesteps": 50,
      "num_inference_steps": "100"
    }
  }' \
  http://localhost:5000/predictions

To learn more, take a look at the Cog documentation.

Output

{
  "completed_at": "2022-10-12T19:13:28.053574Z",
  "created_at": "2022-10-12T19:13:19.441602Z",
  "data_removed": false,
  "error": null,
  "id": "ymcrh3qlzrepri4btsdtlufjua",
  "input": {
    "fp32": true,
    "prompt": "Jon snow from the game of thrones",
    "init_image": "https://replicate.delivery/mgxm/a5f6ab5d-713a-441e-ac98-fac49c74c3b3/205032562_345087143808969_4674423480430035886_n.png",
    "num_outputs": 1,
    "skip_timesteps": 50,
    "num_inference_steps": "100"
  },
  "logs": "Using device: cuda:0\n\n  0%|          | 0/50 [00:00<?, ?it/s]\n  2%|▏         | 1/50 [00:00<00:33,  1.47it/s]\n  4%|▍         | 2/50 [00:01<00:26,  1.83it/s]\n  6%|▌         | 3/50 [00:01<00:23,  1.99it/s]\n  8%|▊         | 4/50 [00:01<00:16,  2.87it/s]\n 10%|█         | 5/50 [00:01<00:11,  3.79it/s]\n 12%|█▏        | 6/50 [00:01<00:09,  4.71it/s]\n 14%|█▍        | 7/50 [00:02<00:07,  5.56it/s]\n 16%|█▌        | 8/50 [00:02<00:06,  6.30it/s]\n 18%|█▊        | 9/50 [00:02<00:05,  6.92it/s]\n 20%|██        | 10/50 [00:02<00:05,  7.42it/s]\n 22%|██▏       | 11/50 [00:02<00:05,  7.80it/s]\n 24%|██▍       | 12/50 [00:02<00:04,  8.08it/s]\n 26%|██▌       | 13/50 [00:02<00:04,  8.30it/s]\n 28%|██▊       | 14/50 [00:02<00:04,  8.45it/s]\n 30%|███       | 15/50 [00:02<00:04,  8.56it/s]\n 32%|███▏      | 16/50 [00:03<00:03,  8.65it/s]\n 34%|███▍      | 17/50 [00:03<00:03,  8.70it/s]\n 36%|███▌      | 18/50 [00:03<00:03,  8.74it/s]\n 38%|███▊      | 19/50 [00:03<00:03,  8.76it/s]\n 40%|████      | 20/50 [00:03<00:03,  8.77it/s]\n 42%|████▏     | 21/50 [00:03<00:05,  5.78it/s]\n 44%|████▍     | 22/50 [00:03<00:04,  6.43it/s]\n 46%|████▌     | 23/50 [00:04<00:03,  7.00it/s]\n 48%|████▊     | 24/50 [00:04<00:03,  7.46it/s]\n 50%|█████     | 25/50 [00:04<00:03,  7.82it/s]\n 52%|█████▏    | 26/50 [00:04<00:02,  8.09it/s]\n 54%|█████▍    | 27/50 [00:04<00:02,  8.29it/s]\n 56%|█████▌    | 28/50 [00:04<00:02,  8.43it/s]\n 58%|█████▊    | 29/50 [00:04<00:02,  8.53it/s]\n 60%|██████    | 30/50 [00:04<00:02,  8.62it/s]\n 62%|██████▏   | 31/50 [00:04<00:02,  8.68it/s]\n 64%|██████▍   | 32/50 [00:05<00:02,  8.73it/s]\n 66%|██████▌   | 33/50 [00:05<00:01,  8.76it/s]\n 68%|██████▊   | 34/50 [00:05<00:01,  8.78it/s]\n 70%|███████   | 35/50 [00:05<00:01,  8.79it/s]\n 72%|███████▏  | 36/50 [00:05<00:01,  8.80it/s]\n 74%|███████▍  | 37/50 [00:05<00:01,  8.80it/s]\n 76%|███████▌  | 38/50 [00:05<00:01,  8.80it/s]\n 78%|███████▊  | 39/50 [00:05<00:01,  8.80it/s]\n 80%|████████  | 40/50 [00:05<00:01,  8.78it/s]\n 82%|████████▏ | 41/50 [00:06<00:01,  5.91it/s]\n 84%|████████▍ | 42/50 [00:06<00:01,  6.55it/s]\n 86%|████████▌ | 43/50 [00:06<00:00,  7.10it/s]\n 88%|████████▊ | 44/50 [00:06<00:00,  7.55it/s]\n 90%|█████████ | 45/50 [00:06<00:00,  7.89it/s]\n 92%|█████████▏| 46/50 [00:06<00:00,  8.15it/s]\n 94%|█████████▍| 47/50 [00:06<00:00,  8.34it/s]\n 96%|█████████▌| 48/50 [00:07<00:00,  8.48it/s]\n 98%|█████████▊| 49/50 [00:07<00:00,  8.57it/s]\n100%|██████████| 50/50 [00:07<00:00,  8.65it/s]\n100%|██████████| 50/50 [00:07<00:00,  6.85it/s]",
  "metrics": {
    "predict_time": 8.385183,
    "total_time": 8.611972
  },
  "output": [
    "https://replicate.delivery/mgxm/c4264c25-682b-4f33-8625-3f14f546e1e0/00000.png"
  ],
  "started_at": "2022-10-12T19:13:19.668391Z",
  "status": "succeeded",
  "urls": {
    "get": "https://api.replicate.com/v1/predictions/ymcrh3qlzrepri4btsdtlufjua",
    "cancel": "https://api.replicate.com/v1/predictions/ymcrh3qlzrepri4btsdtlufjua/cancel"
  },
  "version": "773fe7b990c57d29570b2d6fc71a0f460b129c8c950b5e1b1b9c881581387c98"
}

Generated in

8.4 seconds

Tweak it ShareReport

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Prediction

devxpy/glid-3-xl-stable:7d6a340e

Model

devxpy/glid-3-xl-stable:773fe7b9

efgpyuzn6ravdij3yl42rxkk2a

Status

Succeeded

Source

Web

Hardware

–

Total duration

1m 57s

Created

over 2 years ago

Input

fp32: true
mask
prompt: eiffel tower in paris
outpaint: wider
edit_image
num_outputs: 1
num_inference_steps: "200"

{
  "fp32": true,
  "mask": "https://replicate.delivery/mgxm/e2dfe2c5-7578-421e-ab55-ae9b553243ec/face_mask.png",
  "prompt": "eiffel tower in paris",
  "outpaint": "wider",
  "edit_image": "https://replicate.delivery/mgxm/8a83b0ca-2ea7-4095-8057-499f35a085d8/205032562_345087143808969_4674423480430035886_n.png",
  "num_outputs": 1,
  "num_inference_steps": "200"
}

Install Replicate’s Node.js client library:

npm install replicate

Set the REPLICATE_API_TOKEN environment variable:

export REPLICATE_API_TOKEN=<paste-your-token-here>

Find your API token in your account settings.

Import and set up the client:

import Replicate from "replicate";

const replicate = new Replicate({
  auth: process.env.REPLICATE_API_TOKEN,
});

Run devxpy/glid-3-xl-stable using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "devxpy/glid-3-xl-stable:7d6a340e1815acf2b3b2ee0fcaf830fbbcd8697e9712ca63d81930c60484d2d7",
  {
    input: {
      mask: "https://replicate.delivery/mgxm/e2dfe2c5-7578-421e-ab55-ae9b553243ec/face_mask.png",
      prompt: "eiffel tower in paris",
      outpaint: "wider",
      edit_image: "https://replicate.delivery/mgxm/8a83b0ca-2ea7-4095-8057-499f35a085d8/205032562_345087143808969_4674423480430035886_n.png",
      num_outputs: 1,
      num_inference_steps: "200"
    }
  }
);
console.log(output);

To learn more, take a look at the guide on getting started with Node.js.

Install Replicate’s Python client library:

pip install replicate

Set the REPLICATE_API_TOKEN environment variable:

export REPLICATE_API_TOKEN=<paste-your-token-here>

Find your API token in your account settings.

Import the client:

import replicate

Run devxpy/glid-3-xl-stable using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "devxpy/glid-3-xl-stable:7d6a340e1815acf2b3b2ee0fcaf830fbbcd8697e9712ca63d81930c60484d2d7",
    input={
        "mask": "https://replicate.delivery/mgxm/e2dfe2c5-7578-421e-ab55-ae9b553243ec/face_mask.png",
        "prompt": "eiffel tower in paris",
        "outpaint": "wider",
        "edit_image": "https://replicate.delivery/mgxm/8a83b0ca-2ea7-4095-8057-499f35a085d8/205032562_345087143808969_4674423480430035886_n.png",
        "num_outputs": 1,
        "num_inference_steps": "200"
    }
)
print(output)

To learn more, take a look at the guide on getting started with Python.

Set the REPLICATE_API_TOKEN environment variable:

export REPLICATE_API_TOKEN=<paste-your-token-here>

Find your API token in your account settings.

Run devxpy/glid-3-xl-stable using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

curl -s -X POST \
  -H "Authorization: Bearer $REPLICATE_API_TOKEN" \
  -H "Content-Type: application/json" \
  -H "Prefer: wait" \
  -d $'{
    "version": "7d6a340e1815acf2b3b2ee0fcaf830fbbcd8697e9712ca63d81930c60484d2d7",
    "input": {
      "mask": "https://replicate.delivery/mgxm/e2dfe2c5-7578-421e-ab55-ae9b553243ec/face_mask.png",
      "prompt": "eiffel tower in paris",
      "outpaint": "wider",
      "edit_image": "https://replicate.delivery/mgxm/8a83b0ca-2ea7-4095-8057-499f35a085d8/205032562_345087143808969_4674423480430035886_n.png",
      "num_outputs": 1,
      "num_inference_steps": "200"
    }
  }' \
  https://api.replicate.com/v1/predictions

To learn more, take a look at Replicate’s HTTP API reference docs.

You can run this model locally using Cog. First, install Cog:

brew install cog

If you don’t have Homebrew, there are other installation options available.

Run this to download the model and run it in your local environment:

cog predict r8.im/devxpy/glid-3-xl-stable@sha256:7d6a340e1815acf2b3b2ee0fcaf830fbbcd8697e9712ca63d81930c60484d2d7 \
  -i 'mask="https://replicate.delivery/mgxm/e2dfe2c5-7578-421e-ab55-ae9b553243ec/face_mask.png"' \
  -i 'prompt="eiffel tower in paris"' \
  -i 'outpaint="wider"' \
  -i 'edit_image="https://replicate.delivery/mgxm/8a83b0ca-2ea7-4095-8057-499f35a085d8/205032562_345087143808969_4674423480430035886_n.png"' \
  -i 'num_outputs=1' \
  -i 'num_inference_steps="200"'

To learn more, take a look at the Cog documentation.

Run this to download the model and run it in your local environment:

docker run -d -p 5000:5000 --gpus=all r8.im/devxpy/glid-3-xl-stable@sha256:7d6a340e1815acf2b3b2ee0fcaf830fbbcd8697e9712ca63d81930c60484d2d7
curl -s -X POST \
  -H "Content-Type: application/json" \
  -d $'{
    "input": {
      "mask": "https://replicate.delivery/mgxm/e2dfe2c5-7578-421e-ab55-ae9b553243ec/face_mask.png",
      "prompt": "eiffel tower in paris",
      "outpaint": "wider",
      "edit_image": "https://replicate.delivery/mgxm/8a83b0ca-2ea7-4095-8057-499f35a085d8/205032562_345087143808969_4674423480430035886_n.png",
      "num_outputs": 1,
      "num_inference_steps": "200"
    }
  }' \
  http://localhost:5000/predictions

To learn more, take a look at the Cog documentation.

Output

{
  "completed_at": "2022-10-12T19:45:06.507696Z",
  "created_at": "2022-10-12T19:43:09.581398Z",
  "data_removed": false,
  "error": null,
  "id": "efgpyuzn6ravdij3yl42rxkk2a",
  "input": {
    "fp32": true,
    "mask": "https://replicate.delivery/mgxm/e2dfe2c5-7578-421e-ab55-ae9b553243ec/face_mask.png",
    "prompt": "eiffel tower in paris",
    "outpaint": "wider",
    "edit_image": "https://replicate.delivery/mgxm/8a83b0ca-2ea7-4095-8057-499f35a085d8/205032562_345087143808969_4674423480430035886_n.png",
    "num_outputs": 1,
    "num_inference_steps": "200"
  },
  "logs": "Using device: cuda:0\n\n  0%|          | 0/200 [00:00<?, ?it/s]\n  0%|          | 1/200 [00:00<02:25,  1.36it/s]\n  1%|          | 2/200 [00:01<01:52,  1.76it/s]\n  2%|▏         | 3/200 [00:01<01:41,  1.94it/s]\n  2%|▏         | 4/200 [00:01<01:09,  2.81it/s]\n  2%|▎         | 5/200 [00:01<00:52,  3.72it/s]\n  3%|▎         | 6/200 [00:01<00:41,  4.63it/s]\n  4%|▎         | 7/200 [00:02<00:35,  5.48it/s]\n  4%|▍         | 8/200 [00:02<00:30,  6.23it/s]\n  4%|▍         | 9/200 [00:02<00:27,  6.86it/s]\n  5%|▌         | 10/200 [00:02<00:25,  7.37it/s]\n  6%|▌         | 11/200 [00:02<00:24,  7.77it/s]\n  6%|▌         | 12/200 [00:02<00:23,  8.07it/s]\n  6%|▋         | 13/200 [00:02<00:22,  8.29it/s]\n  7%|▋         | 14/200 [00:02<00:22,  8.44it/s]\n  8%|▊         | 15/200 [00:02<00:21,  8.55it/s]\n  8%|▊         | 16/200 [00:03<00:21,  8.64it/s]\n  8%|▊         | 17/200 [00:03<00:21,  8.70it/s]\n  9%|▉         | 18/200 [00:03<00:20,  8.73it/s]\n 10%|▉         | 19/200 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  "metrics": {
    "predict_time": 79.998951,
    "total_time": 116.926298
  },
  "output": [
    "https://replicate.delivery/mgxm/75523733-bf38-4f0c-bd51-60e9aa87a9df/00000.png"
  ],
  "started_at": "2022-10-12T19:43:46.508745Z",
  "status": "succeeded",
  "urls": {
    "get": "https://api.replicate.com/v1/predictions/efgpyuzn6ravdij3yl42rxkk2a",
    "cancel": "https://api.replicate.com/v1/predictions/efgpyuzn6ravdij3yl42rxkk2a/cancel"
  },
  "version": "773fe7b990c57d29570b2d6fc71a0f460b129c8c950b5e1b1b9c881581387c98"
}

Generated in

80.0 seconds

Tweak it ShareReport

Using device: cuda:0

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Prediction

devxpy/glid-3-xl-stable:7d6a340e

Model

devxpy/glid-3-xl-stable:d53d0cf5

p5icamcxpng5ln4bmo5cfot53e

Status

Succeeded

Source

Web

Hardware

A100 (40GB)

Total duration

13.8s

Created

over 2 years ago

Input

mask
prompt: scarlett johansson wearing earing
edit_image
num_outputs: 1
num_inference_steps: "100"

{
  "mask": "https://replicate.delivery/mgxm/9e666bd2-3d28-4346-b73c-c83cef29f758/mask.png",
  "prompt": "scarlett johansson wearing earing",
  "edit_image": "https://replicate.delivery/mgxm/9a79954b-ce76-4f83-8189-b4d3b28e3543/orig.png",
  "num_outputs": 1,
  "num_inference_steps": "100"
}

Install Replicate’s Node.js client library:

npm install replicate

Set the REPLICATE_API_TOKEN environment variable:

export REPLICATE_API_TOKEN=<paste-your-token-here>

Find your API token in your account settings.

Import and set up the client:

import Replicate from "replicate";

const replicate = new Replicate({
  auth: process.env.REPLICATE_API_TOKEN,
});

Run devxpy/glid-3-xl-stable using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "devxpy/glid-3-xl-stable:7d6a340e1815acf2b3b2ee0fcaf830fbbcd8697e9712ca63d81930c60484d2d7",
  {
    input: {
      mask: "https://replicate.delivery/mgxm/9e666bd2-3d28-4346-b73c-c83cef29f758/mask.png",
      prompt: "scarlett johansson wearing earing",
      edit_image: "https://replicate.delivery/mgxm/9a79954b-ce76-4f83-8189-b4d3b28e3543/orig.png",
      num_outputs: 1,
      num_inference_steps: "100"
    }
  }
);
console.log(output);

To learn more, take a look at the guide on getting started with Node.js.

Install Replicate’s Python client library:

pip install replicate

Set the REPLICATE_API_TOKEN environment variable:

export REPLICATE_API_TOKEN=<paste-your-token-here>

Find your API token in your account settings.

Import the client:

import replicate

Run devxpy/glid-3-xl-stable using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "devxpy/glid-3-xl-stable:7d6a340e1815acf2b3b2ee0fcaf830fbbcd8697e9712ca63d81930c60484d2d7",
    input={
        "mask": "https://replicate.delivery/mgxm/9e666bd2-3d28-4346-b73c-c83cef29f758/mask.png",
        "prompt": "scarlett johansson wearing earing",
        "edit_image": "https://replicate.delivery/mgxm/9a79954b-ce76-4f83-8189-b4d3b28e3543/orig.png",
        "num_outputs": 1,
        "num_inference_steps": "100"
    }
)
print(output)

To learn more, take a look at the guide on getting started with Python.

Set the REPLICATE_API_TOKEN environment variable:

export REPLICATE_API_TOKEN=<paste-your-token-here>

Find your API token in your account settings.

Run devxpy/glid-3-xl-stable using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

curl -s -X POST \
  -H "Authorization: Bearer $REPLICATE_API_TOKEN" \
  -H "Content-Type: application/json" \
  -H "Prefer: wait" \
  -d $'{
    "version": "7d6a340e1815acf2b3b2ee0fcaf830fbbcd8697e9712ca63d81930c60484d2d7",
    "input": {
      "mask": "https://replicate.delivery/mgxm/9e666bd2-3d28-4346-b73c-c83cef29f758/mask.png",
      "prompt": "scarlett johansson wearing earing",
      "edit_image": "https://replicate.delivery/mgxm/9a79954b-ce76-4f83-8189-b4d3b28e3543/orig.png",
      "num_outputs": 1,
      "num_inference_steps": "100"
    }
  }' \
  https://api.replicate.com/v1/predictions

To learn more, take a look at Replicate’s HTTP API reference docs.

You can run this model locally using Cog. First, install Cog:

brew install cog

If you don’t have Homebrew, there are other installation options available.

Run this to download the model and run it in your local environment:

cog predict r8.im/devxpy/glid-3-xl-stable@sha256:7d6a340e1815acf2b3b2ee0fcaf830fbbcd8697e9712ca63d81930c60484d2d7 \
  -i 'mask="https://replicate.delivery/mgxm/9e666bd2-3d28-4346-b73c-c83cef29f758/mask.png"' \
  -i 'prompt="scarlett johansson wearing earing"' \
  -i 'edit_image="https://replicate.delivery/mgxm/9a79954b-ce76-4f83-8189-b4d3b28e3543/orig.png"' \
  -i 'num_outputs=1' \
  -i 'num_inference_steps="100"'

To learn more, take a look at the Cog documentation.

Run this to download the model and run it in your local environment:

docker run -d -p 5000:5000 --gpus=all r8.im/devxpy/glid-3-xl-stable@sha256:7d6a340e1815acf2b3b2ee0fcaf830fbbcd8697e9712ca63d81930c60484d2d7
curl -s -X POST \
  -H "Content-Type: application/json" \
  -d $'{
    "input": {
      "mask": "https://replicate.delivery/mgxm/9e666bd2-3d28-4346-b73c-c83cef29f758/mask.png",
      "prompt": "scarlett johansson wearing earing",
      "edit_image": "https://replicate.delivery/mgxm/9a79954b-ce76-4f83-8189-b4d3b28e3543/orig.png",
      "num_outputs": 1,
      "num_inference_steps": "100"
    }
  }' \
  http://localhost:5000/predictions

To learn more, take a look at the Cog documentation.

Output

{
  "completed_at": "2022-10-27T00:34:59.688826Z",
  "created_at": "2022-10-27T00:34:45.923001Z",
  "data_removed": false,
  "error": null,
  "id": "p5icamcxpng5ln4bmo5cfot53e",
  "input": {
    "mask": "https://replicate.delivery/mgxm/9e666bd2-3d28-4346-b73c-c83cef29f758/mask.png",
    "prompt": "scarlett johansson wearing earing",
    "edit_image": "https://replicate.delivery/mgxm/9a79954b-ce76-4f83-8189-b4d3b28e3543/orig.png",
    "num_outputs": 1,
    "num_inference_steps": "100"
  },
  "logs": "Using device: cuda:0\n\n  0%|          | 0/100 [00:00<?, ?it/s]\n  1%|          | 1/100 [00:00<01:00,  1.64it/s]\n  2%|▏         | 2/100 [00:01<00:49,  1.97it/s]\n  3%|▎         | 3/100 [00:01<00:45,  2.11it/s]\n  4%|▍         | 4/100 [00:01<00:31,  3.03it/s]\n  5%|▌         | 5/100 [00:01<00:23,  4.00it/s]\n  6%|▌         | 6/100 [00:01<00:19,  4.95it/s]\n  7%|▋         | 7/100 [00:01<00:15,  5.82it/s]\n  8%|▊         | 8/100 [00:02<00:13,  6.59it/s]\n  9%|▉         | 9/100 [00:02<00:12,  7.23it/s]\n 10%|█         | 10/100 [00:02<00:11,  7.73it/s]\n 11%|█         | 11/100 [00:02<00:10,  8.12it/s]\n 12%|█▏        | 12/100 [00:02<00:10,  8.40it/s]\n 13%|█▎        | 13/100 [00:02<00:10,  8.62it/s]\n 14%|█▍        | 14/100 [00:02<00:09,  8.78it/s]\n 15%|█▌        | 15/100 [00:02<00:09,  8.89it/s]\n 16%|█▌        | 16/100 [00:02<00:09,  8.98it/s]\n 17%|█▋        | 17/100 [00:03<00:09,  9.03it/s]\n 18%|█▊        | 18/100 [00:03<00:09,  9.07it/s]\n 19%|█▉        | 19/100 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  "metrics": {
    "predict_time": 13.793986,
    "total_time": 13.765825
  },
  "output": [
    "https://replicate.delivery/pbxt/VwdejnwBR21fVUrXdjRECDn6etIQLiW0QdvFlTtUCdXmhwzfA/00000.png"
  ],
  "started_at": "2022-10-27T00:34:45.894840Z",
  "status": "succeeded",
  "urls": {
    "get": "https://api.replicate.com/v1/predictions/p5icamcxpng5ln4bmo5cfot53e",
    "cancel": "https://api.replicate.com/v1/predictions/p5icamcxpng5ln4bmo5cfot53e/cancel"
  },
  "version": "d53d0cf59b46f622265ad5924be1e536d6a371e8b1eaceeebc870b6001a0659b"
}

Generated in

13.8 seconds

Tweak it ShareReport

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 99%|█████████▉| 99/100 [00:12<00:00,  9.15it/s]
100%|██████████| 100/100 [00:12<00:00,  9.16it/s]
100%|██████████| 100/100 [00:12<00:00,  7.95it/s]

Want to make some of these yourself?

Run this model

devxpy / glid-3-xl-stable

Prediction

Input

Output

Prediction

Input

Output

Prediction

Input

Output

Prediction

Input

Output

Logs (thh6qyqtmjbd5adxy7w5s3pfhy)

Logs (ymcrh3qlzrepri4btsdtlufjua)

Logs (efgpyuzn6ravdij3yl42rxkk2a)

Logs (p5icamcxpng5ln4bmo5cfot53e)